This paper empirically examines the performance implications of cross-border mergers and acquisitions (CB M&As) in the global banking sector, focusing on the returns of both bidders and targets across multiple event windows. Using an international sample from 1997 to 2023, we compute cumulative abnormal returns (CARs) over short- and long-term horizons and employ logit model to identify key cross-border deal determinants. While target firms consistently earn significant positive abnormal returns, acquirer gains materialize primarily in the long run. Notably, we find that acquirer performance is shaped by CEO compensation incentives and board structure, particularly the number of managing directors. Acquirers with performance-aligned CEO pay and smaller, more independent boards tend to achieve superior post-deal outcomes. Additionally, we identify regulatory arbitrage as a driver of some CB-M&As, where firms seek jurisdictions with looser oversight; however, these deals often underperform due to governance and integration challenges. Our findings highlight the interplay between governance, regulation, and long-term value creation in CB M&As.
Nano-biosensors have upended the approach of measuring breast cancer recurrence in a non-invasive way because it can detect biomolecular changes at very tiny levels in the patient samples. The study reviewed the use of nano-biosensor technology in conjunction with machine learning algorithms to predict recurrence risk based on multi-parametric biomarker data. The features of population of patients, i.e., circulating tumor cells, exosome population, miRNA expression, protein biomarkers, and biosensor-detected electrochemical and optical signals were modeled by creating a synthetic dataset. To perform better prediction, demographic and clinical factors, i.e., age of the patient, body mass index, and treatment history, were also included. Thus, the machine learning model is based on such correlations of these biosensor-derived features and the recurrence risk, which allows for estimating the likelihood of diseases returning. Correlations showed that the major outputs of the nano-biosensors such as electrochemical and optical signals are significantly correlated (0.5-0.8) with the recurrence probability, which confirms their value as predictive signals. Scatter plot and distribution graphs also identified trends and variations of the patient sub-groups, indicating the nano-biosensor data ability to be used to stratify patients and monitor them individually. This method offers a scalable, non-invasive method of early cancer recurrence detection in the breast through convergence between nano-biosensor measurements and sophisticated predictive modeling strategies. This promises to change patient outcomes by improving nanoscale diagnostics and permitting constant patient monitoring without invasive approaches. This paper has proven that features based on nano-biosensors combined with machine learning can attain moderate to high predictive power with respect to breast cancer recurrence risks, thus showing non-invasive monitoring on a nanoscale is promising.
This study provides a comprehensive evaluation of the competitiveness and efficiency of 52 Cambodian commercial banks through the integration of a multi-criteria Enterprise Competitiveness Index (ECI), twostage Data Envelopment Analysis (DEA), and a Back Propagation Neural Network (BPNN). Grounded in the Resource-Based View (RBV), Dynamic Capability Theory (DCT), and the Resource-Capability-Performance (RCP) framework, the model captures both internal efficiency and broader strategic potential. The ECI offers a multidimensional benchmark encompassing financial, marketing, operational, and human resource dimensions. Two-stage DEA evaluates how effectively banks convert inputs (e.g., advertising, labor quality) into intermediate outputs (e.g., productivity measures) and subsequently into financial performance. A BPNN is incorporated to model non-linear relationships and forecast profitability outcomes with over 85 % prediction accuracy. Findings reveal significant variation in banks' ability to translate operational productivity into financial returns, indicating potential strategic misalignments. Some banks exhibit high resource utilization but fail to achieve commensurate profitability, while others demonstrate strong financial outcomes despite weaker operational foundations. The integration of the ECI and DEA highlights the distinction between technical efficiency and long-term competitiveness, while the BPNN enhances strategic foresight by capturing hidden patterns across performance variables. This multimethod approach offers valuable insights for regulators and managers in emerging financial markets, particularly in addressing governance, resource allocation, and sustainable growth strategies. The study not only fills methodological gaps in traditional banking efficiency models but also provides a robust tool for adaptive strategic planning in Cambodia's rapidly evolving banking landscape. (c) 2025 The Authors. Published by Elsevier B.V. on behalf of College of Management, National Cheng Kung University. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
Printed circuit heat exchangers (PCHEs) are known for their compact design and efficient heat transfer characteristics; however, optimizing their flow and heat transfer using the finite volume method is computationally inefficient. This study combines Proper orthogonal decomposition (POD), Randomized singular value decomposition (RSVD), and Gaussian process regression (GPR) to develop a reduced-order model with enhanced optimization efficiency. Using Latin hypercube sampling, operational parameters are fed into Fluent to generate sample data for the reduced-order model, which predicts the PCHE flow and heat transfer characteristics under new operational conditions. The model efficiently decomposes high-rank matrices and accurately predicts the PCHE multi-physics field distributions. Within the sample space, 84.47 % of grid points exhibit relative percentage errors below 2 % for temperature field predictions, while 82.31 % of data points demonstrate velocity magnitude errors within 4 %. Notably, 96.4 % of points show velocity vector angular deviations under 2 degrees. For extrapolated samples outside the sample space, the GPR model yields average relative percentage errors of 8.1 % for temperature field and 42.3 % for velocity magnitude, with an average velocity direction deviation angle of 22.4 degrees. Through detailed analysis of extrapolation cases, we established that the RMSE growth of both velocity and temperature fields follows approximately quadratic functions with increasing extrapolation distance and the errors become particularly pronounced at structural edges with steep physical gradients. To maintain prediction accuracy, strict limitations must be imposed on the allowable deviation distance of extrapolated samples from the original sample space. The GPR-based reduced-order model achieves a 3.3-fold memory reduction (1889.2 MB vs 6250 MB) and 70 x faster computation (6.5 s vs 7.5 min) compared to conventional CFD simulations using Fluent. Overall, the model offers valuable insights for simplifying three-dimensional multi-physical field computations.
This study evaluates the competitiveness of 269 listed firms in the Philippines using an integrated framework that combines multi-criteria analysis, two-stage Data Envelopment Analysis (DEA), and Back Propagation Neural Network (BPNN). Drawing on the Resource-Based View (RBV) and Dynamic Capability Theory (DCT), the research investigates how functional capabilities in marketing, operations, and human resources influence both profitability-based and market value-based performance. The Enterprise Competitiveness Index (ECI) synthesizes performance (productivity and profitability) and potential (market image, stability, and growth), offering a comprehensive and standardized basis for ranking firms. Results reveal significant misalignments: some firms rank high in short-term performance but low in long-term potential, while others show strong future competitiveness despite current inefficiencies. These insights emphasize the importance of integrated assessments rather than relying solely on financial metrics. Two-stage DEA results further show that many firms convert capabilities into profitability efficiently but struggle to transform that profitability into market value. The DEA-BPNN framework achieves over 90 % predictive accuracy when DEA efficiency scores are included, demonstrating the model's ability to capture non-linear relationships and provide reliable forecasting. This study contributes a novel, theory-driven and empirically validated approach to measuring and predicting firm competitiveness. By integrating ranking, benchmarking, and predictive modeling, it offers a valuable tool for strategic decision-making and performance management in emerging markets. (c) 2025 The Authors. Published by Elsevier B.V. on behalf of College of Management, National Cheng Kung University. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).
